saikat Gochhait
@siu.edu.in
Assistant Professor Academic Level 12 7th Pay CPC
Symbiosis International Deemed University
Dr. Saikat Gochhait teaches at Symbiosis Institute of Digital & Telecom Management, Symbiosis International Deemed University Pune, India and Neurosciences Research Institute-Samara State Medical University, Russia. He is Ph.D and Post-Doctoral Fellow from the UEx, Spain and National Dong Hwa University, Taiwan. He was Awarded DITA and MOFA Fellowship in 2017 and 2018. His research publication with foreign authors is indexed in Scopus, ABDC, and Web of Science. He is a Senior IEEE member.
EDUCATION
Post Doctoral Fellow - Uex, Spain
Post Doctoral Fellow - National Dong Hwa University, Taiwan
PhD - Sambalpur University
RESEARCH INTERESTS
Technology Management
Marketing
Healthcare
Entrepreneurship
FUTURE PROJECTS
Neurosciences
NeuroMarketing
Applications Invited
Collaborators
Entreprenuership
Women Entrepreneurs
Applications Invited
Collaborators
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Hashmat Fida, Harsh Sadawarti, Binod Kumar Mishra, Ashaq Hussain Bhat, Saikat Gochhait, and Sami Alshmrany
Springer Science and Business Media LLC
Saikat Gochhait, Renuka Lenka, and Aidin Salamzadeh
Uniwersytet Ekonomiczny w Krakowie - Krakow University of Economics
Amitesh Prakash, Saikat Gochhait, Prabakaran Raghavendran, and Tharmalingam Gunasekar
IGI Global
Modern simulation models of virtual reality (VR) and augmented reality (AR) are, at present, enhancing medical education. Users can engage structures in real-time 3D interaction using virtual reality. Advanced technologies in haptics, display systems, and motion detection help the user to achieve an experience of realism with interactive features; hence VR is best suited for practical procedures training. As such, applications of VR are found more in surgeries and other interventional procedures. The application of AR allows for the modification or augmentation of the physical environment by combining virtual data and structures with physical objects. It seems useful to have AR applications as an integral part of our knowledge concerning physiological and anatomical processes. Numerous VR and AR applications using various hardware platforms and in diverse settings have been the subject of experiments aiming to prove their realism and didactic value. Some history of VR AR in medicine can be found in this chapter, and some guide ideals and norms rule them.
Prakash Chand Thakur, Dinesh Thakur, Tharmalingam Gunasekar, Prabakaran Raghavendran, and Saikat Gochhait
IGI Global
This paper presents a cryptographic framework that incorporates the Anuj Transform and the congruence modulo operator to improve data security and allow for efficient information retrieval. The methodology, based on the mathematical properties of the Anuj Transform and its inverse, is used in designing strong encryption and decryption techniques. The additional security of encrypted messages is assured by the incorporation of the congruence modulo operator. Comprehensive analyses are carried out through graphs and evaluations over the principal parameters: encryption precision, computing speed, resistance, scalability. The outcome shows how well the Anuj Transform coupled with the congruence modulo operator can really help to face modern problems within cryptography.
Saikat Gochhait
IGI Global
Although online social platforms are vulnerable to private information leakage, third parties can still do want with your data easily and consent. With Indeed the rapid spread of information today and changed role for social media, people more commonly worry over privacy. India's Digital Personal Data Protection Act 2023 seeks to cope with this risk by strengthening data protection. The legal framework must evolve constantly to guarantee the privacy and dignity of its recipients, permitting properly informed control over personal information in a world increasingly digital all the time.
Prabakaran Raghavendran, Tharmalingam Gunasekar, and Saikat Gochhait
IEEE
This study examines the emergent interest in accurate Solana price predictions among depositors, buyers, and governmental bodies. Solana, a groundbreaking cryptocurrency known for its reorganized nature, has appealed substantial responsiveness. Applying progressive artificial neural networks (ANN), we aim to projection Solana prices by leveraging their capacity to understand the intricate and impulsive outlines typical of cryptocurrency markets. Our pioneering line of attack encompasses exploring diverse lag conformations over specific time intervals to optimize forecast accuracy and timeliness. Through rigorous validation, focusing on root mean square error as a key performance metric, our ANN model dependably outclasses traditional prediction methods. These findings offer valuable insights for individuals, industries, and governmental bodies directing the intricacies of the cryptocurrency landscape. Furthermore, we introduce an algorithm and provide Python code to determine the execution of our approach for forecasting Solana prices.
Manisha Paliwal, Omkar Jagdish Bapat, and Saikat Gochhait
Philippine Normal University
The field of higher education in India is plagued by various fraudulent institutions and actors who profit at the expense of students and parents, for example, by issuing fake course completion certificates, which adversely affect the quality of teacher education. This paper presents a possible solution in the form of a model in which a digital record of the student's academic progress is created on the blockchain, which is then used to create a non-falsifiable tokenized version of the student's certificate of completion with a QR code-based mechanism that helps to improve the quality of teacher education in the course completion certificate process. Through this model, cases of certificate fraud can be reduced, and confidence in the quality of education that students receive in the institutions can be ensured. Publication History Version of Record online: December 28, 2023 Manuscript accepted: December 19, 2023 Manuscript revised: December 14, 2023 Manuscript received: August 7, 2023
Prabakaran Raghavendran, Tharmalingam Gunasekar, and Saikat Gochhait
European Alliance for Innovation n.o.
This paper examines various types of fractional differential equations using fractional calculus methods. It extends the classical Frobenius method and introduces key theorems that apply the Ramadan Group transform and other techniques. Additionally, the research incorporates machine learning, specifically neural networks, to solve these equations. The paper demonstrates that machine learning can enhance the solution process through data generation, model design, and optimization. Examples provided illustrate how combining traditional methods with machine learning can effectively solve fractional differential equations.
M. Vijai, T. Ananth Kumar, P. Kanimozhi, and Saikat Gochhait
IEEE
Lane detection and tracking are crucial for modern vehicle navigation systems, especially for ADAS and autonomous vehicles. Traditional methods often fail under adverse conditions such as poor lighting, bad weather, and inconsistent road markings. This paper presents a novel approach using YOLOv5, an advanced object detection model known for its real-time performance and accuracy, to detect lane boundaries directly from images. We improved its robustness in challenging scenarios by adapting YOLOv5 for lane detection and introducing innovative post-processing techniques. These techniques include refining lane predictions, handling occlusions, and reducing noise. Extensive experiments on datasets from various conditions (daytime, nighttime, and adverse weather) show that our method outperforms existing approaches. The proposed YOLOv5-based system offers a promising solution for real-world driving challenges, enhancing the precision and dependability of lane recognition and tracking and positively impacting road safety and autonomous vehicle technologies.
Rushali Garg, Anuradha S. Kanade, Prabha Kiran, and Saikat Gochhait
Springer Nature Singapore
Palla Manoj Babu, Ashish Kumar, P. Venkata Subbaiah, V. Mouneswari, Prabha Kiran, and Saikat Gochhait
Springer Nature Singapore
Apurva Kale, Gaurav Gupta, Priyanshi Sharma, Piyush Ranjan, Dhruv Seth, and Saikat Gochhait
Springer Nature Singapore
Anurag Sinha, Jeet Verma, Ahmed Alkhayyat, Hassan Raza Mahmood, Shehroz Ahmed, and Saikat Gochhait
IEEE
Samiksha Pedewad, Anjali Thakur, Pradeep Chintale, Gaurav Gupta, Arun Pandiyan Perumal, and Saikat Gochhait
Springer Nature Singapore
Rushikesh Bhandari, Gaurav Gupta, Harsha Vardhan Sanne, Arun Pandiyan Perumal, Anandaganesh Balakrishnan, and Saikat Gochhait
Springer Nature Singapore
K. Y. Vinay, Veena Grover, Gaurav Gupta, Jitendra Sharma, Dinesh Reddy Chittibala, Raj Mehta, and Saikat Gochhait
Springer Nature Singapore
Pradeep Chintale, Pranay Waghmare, Shivi Khanna, Ankur Mahida, Gaurav Gupta, Shantanu Kumar, and Saikat Gochhait
Springer Nature Singapore
Kranti Shingate, Veena Grover, Shivi Khanna, Gaurav Gupta, Pradeep Chintale, HarshaVardhan Nerella, C. H. Vanipriya, and Saikat Gochhait
Springer Nature Singapore
Saikat Gochhait, Deepak K. Sharma, and Mrinal Bachute
University of Basrah - College of Engineering
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential features from electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integrates a feature extraction module, densely connected residual block (DCRB), long short-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decision making processes, making it a valuable tool for stakeholders in the electricity sector.
Shashank Mittal, Priyank Kumar Singh, Saikat Gochhait, and Shubham Kumar
IGI Global
Accurate disease prognosis is crucial for improved healthcare outcomes. Artificial intelligence (AI) offers immense potential in this domain, but traditional “black-box” models lack interpretability. This chapter explores the integration of Explainable AI (XAI) with Green AI, a resource-efficient and sustainable approach to AI development. They discuss how XAI can enhance trust in Green AI models for disease prognosis, mitigate potential biases, and promote responsible AI development. They highlight the challenges of balancing interpretability with efficiency and propose future research directions to unlock the full potential of XAI for Green AI-powered disease prognosis. This approach has the potential to revolutionize healthcare by providing accurate, transparent, and environmentally friendly tools for early disease detection and improved patient outcomes.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
GRANT DETAILS
Department of Science and Industrial Research , Govt of India with Grant of Rs 13,000,00
Ministry of Foreign Affairs, Taiwan with Grant of Rs 12,000,00
University of Deusto, Spain with Research Grant of Rs 2,000,00
University of Extremadura, Spain with Research Grant of Rs 2,000,00
Samara State Medical University, Russia with Research Visit grant of Rs 2,500,00
Symbiosis International Deemed University with Travel and Research Grant of 4,000,000
INDUSTRY EXPERIENCE
IFGL Refractories Ltd